Title :
Online learning of electric vehicle consumers´ charging behavior with missing data
Author :
Soltani, Nasim Yahya ; Giannakis, Georgios B.
Author_Institution :
Dept. of ECE, Univ. of Minnesota, Minneapolis, MN, USA
Abstract :
Learning in the presence of missing data is a pervasive problem in statistical data analysis. This paper deals with tracking the dynamic charging behavior of electric vehicle consumers, when some of the consumers´ consumption decisions are missing. The problem is then formulated as an online classification task with missing labels. An online algorithm is proposed to jointly impute the missing data while at the same time learn from the complete data using an online convex optimization approach.
Keywords :
behavioural sciences computing; convex programming; data analysis; electric vehicles; learning (artificial intelligence); pattern classification; power engineering computing; electric vehicle consumer charging behavior; missing data imputation; online classification task; online convex optimization approach; online learning; statistical data analysis; Companies; Convex functions; Elasticity; Energy exchange; Heuristic algorithms; Information processing; Smart grids; Smart Grid; conditional random field; misses; online convex optimization;
Conference_Titel :
Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on
Conference_Location :
Atlanta, GA
DOI :
10.1109/GlobalSIP.2014.7032115
